]
continuous_cols = [
"latitude", "longitude", "security_deposit", "extra_people"
]
already_standard = ["latitude", "longitude"]
text_col = "description"
word_vectors_path = "data/glove.6B/glove.6B.100d.txt"
img_col = "id"
img_path = "data/airbnb/property_picture"
target = "yield"
target = df[target].values
wide_preprocessor = WidePreprocessor(wide_cols=wide_cols,
crossed_cols=crossed_cols)
X_wide = wide_preprocessor.fit_transform(df)
tab_preprocessor = TabPreprocessor(
embed_cols=cat_embed_cols, # type: ignore[arg-type]
continuous_cols=continuous_cols,
already_standard=already_standard,
)
X_tab = tab_preprocessor.fit_transform(df)
text_processor = TextPreprocessor(word_vectors_path=word_vectors_path,
text_col=text_col)
X_text = text_processor.fit_transform(df)
image_processor = ImagePreprocessor(img_col=img_col, img_path=img_path)
X_images = image_processor.fit_transform(df)
cat_embed_cols = []
for col in train.columns:
if train[col].nunique() >= 5 and train[col].nunique(
) < 200 and col != "target":
cat_embed_cols.append(col)
num_cols = [
c for c in train.columns if c not in cat_embed_cols + ["target"]
]
wide_cols = []
for col in train.columns:
if train[col].nunique() < 40 and col != "target":
wide_cols.append(col)
prepare_wide = WidePreprocessor(wide_cols)
X_wide_train = prepare_wide.fit_transform(train)
X_wide_valid = prepare_wide.transform(valid)
prepare_tab = TabPreprocessor(
embed_cols=cat_embed_cols,
continuous_cols=num_cols,
for_tabtransformer=True,
scale=False,
)
X_tab_train = prepare_tab.fit_transform(train)
X_tab_valid = prepare_tab.transform(valid)
y_train = train.target.values
y_valid = valid.target.values
wide = Wide(wide_dim=np.unique(X_wide_train).shape[0])
("cancellation_policy", 16),
]
continuous_cols = [
"latitude", "longitude", "security_deposit", "extra_people"
]
already_standard = ["latitude", "longitude"]
df["yield_cat"] = pd.cut(df["yield"],
bins=[0.2, 65, 163, 600],
labels=[0, 1, 2])
df.drop("yield", axis=1, inplace=True)
target = "yield_cat"
target = np.array(df[target].values)
prepare_wide = WidePreprocessor(wide_cols=wide_cols,
crossed_cols=crossed_cols)
X_wide = prepare_wide.fit_transform(df)
prepare_deep = DensePreprocessor(embed_cols=cat_embed_cols,
continuous_cols=continuous_cols)
X_deep = prepare_deep.fit_transform(df)
wide = Wide(wide_dim=X_wide.shape[1], pred_dim=3)
deepdense = DeepDense(
hidden_layers=[64, 32],
dropout=[0.2, 0.2],
deep_column_idx=prepare_deep.deep_column_idx,
embed_input=prepare_deep.embeddings_input,
continuous_cols=continuous_cols,
)
model = WideDeep(wide=wide, deepdense=deepdense, pred_dim=3)
model.compile(method="multiclass", metrics=[Accuracy, F1Score])
("occupation", 16), ("native-country", 32)]
deep_continuous_column_list = ["age", "hours-per-week"]
# Target
target_column_list = ["income_label"]
target = train_df[target_column_list].values
"""
Preprocessing
"""
# Wide
wide_preprocessor = WidePreprocessor(wide_cols=wide_columns_list,
crossed_cols=wide_cross_column_list)
x_wide = wide_preprocessor.fit_transform(train_df)
# Deep
tab_preprocessor = TabPreprocessor(
embed_cols=deep_embedding_columns_list,
continuous_cols=deep_continuous_column_list)
x_deep = tab_preprocessor.fit_transform(train_df)
"""
Model 구조 정의
"""
# Model
wide = Wide(wide_dim=np.unique(x_wide).shape[0], pred_dim=1)
deeptabular = TabMlp(mlp_hidden_dims=[64, 32],
column_idx=tab_preprocessor.column_idx,
embed_input=tab_preprocessor.embeddings_input,
continuous_cols=deep_continuous_column_list)